Literature DB >> 35339256

Brain Tumor Imaging: Applications of Artificial Intelligence.

Muhammad Afridi1, Abhi Jain2, Mariam Aboian2, Seyedmehdi Payabvash3.   

Abstract

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
Copyright © 2022 Elsevier Inc. All rights reserved.

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Year:  2022        PMID: 35339256      PMCID: PMC8961005          DOI: 10.1053/j.sult.2022.02.005

Source DB:  PubMed          Journal:  Semin Ultrasound CT MR        ISSN: 0887-2171            Impact factor:   1.875


  79 in total

1.  Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

Authors:  Zenghui Qian; Yiming Li; Yongzhi Wang; Lianwang Li; Runting Li; Kai Wang; Shaowu Li; Ke Tang; Chuanbao Zhang; Xing Fan; Baoshi Chen; Wenbin Li
Journal:  Cancer Lett       Date:  2019-03-13       Impact factor: 8.679

2.  Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis.

Authors:  Moran Artzi; Idan Bressler; Dafna Ben Bashat
Journal:  J Magn Reson Imaging       Date:  2019-01-11       Impact factor: 4.813

3.  A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.

Authors:  Zijian Zhang; Jinzhong Yang; Angela Ho; Wen Jiang; Jennifer Logan; Xin Wang; Paul D Brown; Susan L McGovern; Nandita Guha-Thakurta; Sherise D Ferguson; Xenia Fave; Lifei Zhang; Dennis Mackin; Laurence E Court; Jing Li
Journal:  Eur Radiol       Date:  2017-11-24       Impact factor: 5.315

4.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

Authors:  Zhi-Cheng Li; Hongmin Bai; Qiuchang Sun; Qihua Li; Lei Liu; Yan Zou; Yinsheng Chen; Chaofeng Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-03-21       Impact factor: 5.315

5.  Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

Authors:  Mehmet Günhan Ertosun; Daniel L Rubin
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

6.  Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach.

Authors:  Girolamo Crisi; Silvano Filice
Journal:  J Neuroimaging       Date:  2020-05-06       Impact factor: 2.486

7.  Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma.

Authors:  Niha Beig; Jay Patel; Prateek Prasanna; Virginia Hill; Amit Gupta; Ramon Correa; Kaustav Bera; Salendra Singh; Sasan Partovi; Vinay Varadan; Manmeet Ahluwalia; Anant Madabhushi; Pallavi Tiwari
Journal:  Sci Rep       Date:  2018-01-08       Impact factor: 4.379

8.  Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas.

Authors:  Paul Eichinger; Esther Alberts; Claire Delbridge; Stefano Trebeschi; Alexander Valentinitsch; Stefanie Bette; Thomas Huber; Jens Gempt; Bernhard Meyer; Juergen Schlegel; Claus Zimmer; Jan S Kirschke; Bjoern H Menze; Benedikt Wiestler
Journal:  Sci Rep       Date:  2017-10-17       Impact factor: 4.379

9.  XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.

Authors:  Nguyen Quoc Khanh Le; Duyen Thi Do; Fang-Ying Chiu; Edward Kien Yee Yapp; Hui-Yuan Yeh; Cheng-Yu Chen
Journal:  J Pers Med       Date:  2020-09-15

10.  Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading.

Authors:  Linmin Pei; Karra A Jones; Zeina A Shboul; James Y Chen; Khan M Iftekharuddin
Journal:  Front Oncol       Date:  2021-07-01       Impact factor: 6.244

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  1 in total

1.  The Current Landscape of Clinical Predictions from Brain Tumor Imaging.

Authors:  Anael Rizzo; Ricky Savjani
Journal:  Radiol Imaging Cancer       Date:  2022-05
  1 in total

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